用七步临床推理框架让大模型答对医学开放问题
Structured Outputs Enable General-Purpose LLMs to be Medical Experts
- 模拟医生诊断流程，分七步引导模型思考
- 在MedLFQA上达成85.8的最高事实性得分
- 小模型也能受益，适合医疗AI落地应用
医学问答是评估大语言模型临床知识编码能力及医学应用潜力的关键任务。尽管在选择题上表现良好，大模型在开放式医学问题上常出现危险幻觉或关键信息遗漏。现有方法依赖领域微调，成本高且难扩展。本文提出一种基于结构化医学推理的新方法，通过模拟临床诊断的七步认知过程，无需额外训练即可提升回答的准确性和完整性。在MedLFQA基准测试中，该方法达到85.8的事实性分数，优于微调模型。值得注意的是，该效果可迁移至小型模型，凸显其高效与可扩展性。代码与数据集已公开。
原文摘要 · Abstract (English)
Medical question-answering (QA) is a critical task for evaluating how effectively large language models (LLMs) encode clinical knowledge and assessing their potential applications in medicine. Despite showing promise on multiple-choice tests, LLMs frequently struggle with open-ended medical questions, producing responses with dangerous hallucinations or lacking comprehensive coverage of critical aspects. Existing approaches attempt to address these challenges through domain-specific fine-tuning, but this proves resource-intensive and difficult to scale across models. To improve the comprehensiveness and factuality of medical responses, we propose a novel approach utilizing structured medical reasoning. Our method guides LLMs through an seven-step cognitive process inspired by clinical diagnosis, enabling more accurate and complete answers without additional training. Experiments on the MedLFQA benchmark demonstrate that our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models. Notably, this improvement transfers to smaller models, highlighting the method's efficiency and scalability. Our code and datasets are available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。